CN116992600A - Multi-constraint blade section line partition characteristic point acquisition method - Google Patents
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Abstract
The application provides a multi-constraint blade section line partition characteristic point acquisition method, which comprises the following steps: 1) Generating a blade section line; 2) Generating a point set according to the obtained blade section line; 3) Deriving coordinates and curvature information of the point set; 4) Sorting the point sets and amplifying the curvature values; 5) And traversing the whole point set, and respectively calculating angle constraint, curvature difference ratio constraint, curvature difference constraint and curvature value ratio constraint by taking every three points as a group of data. When the four constraints are satisfied simultaneously, the front and rear edge boundary points of the blade section line are obtained. The method solves the problem that the front and rear edge boundary points of the section line of the blade cannot be accurately obtained when the existing blade solid model divides the arc line.
Description
Technical Field
The application relates to the field of machine manufacturing and reverse engineering, in particular to a multi-constraint blade section line partition characteristic point acquisition method, and belongs to an auxiliary method for improving reverse modeling accuracy and machining precision.
Background
The blade parts are applied to aeroengines in a large number, and some blade parts which are deformed compared with theoretical digital and analog are mainly processed by manual correction, polishing and other operations to improve the processing precision of the blade, and the processing precision depends on the operation skills and experience of workers, so that the production period of the parts is longer and the consistency of the surface profile quality is poor. The machining technology based on reverse engineering is an effective solution for realizing accurate machining of the blade parts, and has the core of accurately acquiring the actual profile of the parts and carrying out model reconstruction on the deformation parts of the parts to regenerate a machining tool path, but the partition of the front edge, the rear edge, the blade basin and the blade back on the blade parts often needs to observe curvature combs of blade section lines to find boundary points, and the overall finding efficiency is lower.
The application of the patent number CN113997125A discloses an on-machine measurement-based blade section line self-adaptive reconstruction method, which is characterized in that blade profile section line data are acquired in an on-machine measurement mode, automatic registration is performed, an actual section line of a blade is self-adaptively reconstructed, errors between the blade and the actual blade are ensured, an on-machine measurement technology and automatic registration are adopted, a blade alignment step in self-adaptive machining is avoided, and the machining efficiency is improved. In the application, the method for extracting the boundary points comprises the steps of firstly calculating the curvature of the sharp point, searching to the left side and the right side respectively according to the sharp point, and taking the boundary points of the front edge and the rear edge with the leaf basin and the leaf back when the curvature of the searching point is less than or equal to 1/5 of the curvature of the sharp point. However, this method of extracting the demarcation point is inaccurate and cannot filter curvature jump points that easily cause interference. The reason is as follows: note that the search point curvature in this application is A1, A2 … … Ai (i=1, 2, … … n), the sharp point curvature is a, and as can be seen from the specification, the search condition in this application is An <0.2A, and since the curvature and the curvature radius are reciprocal, the inequality relationship that can convert this condition into the curvature radius is more intuitive, that is, bn >5*B, and the symbol B represents the curvature radius. The radius of curvature at the cusp of the blade is typically only a few millimeters, whereas the radius of curvature at the back of the leaf basin is as high as tens or even hundreds of millimeters, and there are a large number of points that meet the search criteria in this application, so that the search criteria are not universal, e.g., there may be no search for the unique four demarcation points.
The application of patent number CN114936389B discloses a method for constructing a mean camber line and dividing geometric features of a blade section line, which comprises the steps of traversing a measuring blade to obtain the mean camber line, extracting a circle center under the constraint of the increment of the rest arc length and the mean camber line construction point based on geometric information of a design edge head, and dividing the measuring section line based on the circle center projected on the mean camber line and the arc angle of the design blade edge head. In this application, it is necessary to construct a plurality of inscribed circles to obtain the separation points, and the present application is applicable only to blades having circular arcs at the front and rear edges of the blade, and it is not possible to extract the partition points for blades having irregular shapes at the front and rear edges, so that the search condition is not universal.
Disclosure of Invention
Aiming at the defects of the prior art method, the application provides a multi-constraint blade section line partition characteristic point acquisition method, and aims at the problems that the front edge and the rear edge of a blade section line are not accurately acquired when the existing blade solid model divides arcs by acquiring the point set of the section line on the blade solid model, carrying out data processing on the detailed information of the point set and calculating constraint conditions, thereby accurately acquiring the front edge and the rear edge of the blade section line and dividing arcs on the front edge, the rear edge, the blade basin and the blade back.
In order to achieve the technical purpose, the application adopts the following technical scheme:
a multi-constraint blade section line partition characteristic point acquisition method comprises the following steps:
s1, generating a section line:
obtaining a blade section line from the blade solid model, wherein the blade section line is an intersection line between a reference plane and the blade solid model, and the reference plane is perpendicular to the extension direction of the blade body;
s2, generating a point set:
sequentially sampling the section lines of the blade along the extending direction of the section lines of the blade in a mode of equal arc length, taking the two-dimensional coordinates of each sampling point and the curvature value of the section line at the sampling point as a group of sampling arrays, and generating a point set for export;
s3, preprocessing a point set:
ordering the sampling arrays in the point set according to the sampling time sequence, uniformly amplifying the curvature values in the sampling arrays, and deriving a preprocessed point set;
s4, traversing all sampling arrays in the preprocessed point set, selecting one sampling point at any time, and taking the sampling point and two sampling points which are continuously arranged behind the sampling point as a group of data;
s5, angle constraint judgment:
the first point in the group points to the second point to form a first vector, the second point points to the third point to form a second vector, whether the included angle between the first vector and the second vector meets a preset angle threshold range or not is judged, if not, the current sampling point is discarded, and the step S4 is carried out, and if yes, the step S6 is carried out;
s6, constraint judgment of curvature difference ratio:
sequentially calculating absolute values of curvature difference values of two adjacent sampling points, calculating a ratio between the absolute values of the two curvature difference values, judging whether the ratio meets a preset ratio threshold range, if not, discarding the current sampling point, and turning to step S4, if so, turning to step S7;
s7, difference constraint judgment of curvature difference values:
sequentially calculating absolute values of curvature difference values of two adjacent sampling points, calculating a difference value between the two curvature difference absolute values, judging whether the difference value meets a preset difference value threshold range, if not, discarding the current sampling point, and turning to step S4, if so, turning to step S8;
s8, constraint judgment of the ratio of the curvature values:
calculating to obtain curvature value ratio of the third sampling point and the second sampling point and curvature value ratio of the first sampling point and the second sampling point, respectively judging whether the curvature value ratio of the first sampling point and the second sampling point is larger than a preset ratio threshold value, if so, outputting the current sampling point as a characteristic point, otherwise, discarding the current sampling point, and turning to step S4;
and S9, repeating the steps S4 to S8 until all the sampling points are processed, outputting all the characteristic points, and obtaining the boundary points of the front edge and the rear edge of the section line of the blade.
Further, in step S2, the distance between adjacent sampling points is less than 0.02mm.
Further, in step S3, the curvature values in the sampling array are amplified uniformly, and the amplification factor is selected so that the gradient angles of all curvature abrupt changes are greater than or equal to 45 degrees and less than or equal to 135 degrees.
Further, in step S5, the preset angle threshold ranges from 45 ° to 135 °.
Further, in step S6, the determining process of the curvature difference ratio constraint includes:
the curvature difference ratio is calculated by the following formula：
in the formula ,the curvature value of the i-th point is indicated,the curvature value of the i+1th point is represented,a curvature value representing the i+2th point;
if the ratio of curvature differencesGreater than 10, or a ratio of curvature differencesAnd if the curvature difference is smaller than 0.1, judging that the current sampling point meets the curvature difference ratio constraint.
Further, in step S7, the determining process of the difference constraint of the curvature difference value includes:
calculating the difference between the curvature differences：
wherein ,the curvature value of the i-th point is indicated,the curvature value of the i+1th point is represented,a curvature value representing the i+2th point;
calculating the difference between the curvature differences：
If D1 or D2 is larger than 0, judging that the current sampling point meets the difference constraint of the curvature difference value.
Further, in step S8, the preset ratio threshold is 1.5.
Further, in step S8, according to all the feature points, the boundary points of the front edge and the rear edge of the blade section line are obtained, and the front edge, the rear edge, the blade basin and the blade back area of the blade section line are divided.
Compared with the prior art, the application has the following beneficial effects:
the application provides a model reverse modeling method for the finish machining of a thin-wall part, and provides a multi-constraint method for obtaining the regional characteristic points of the section line of the blade, aiming at the defects of the prior art method.
Drawings
FIG. 1 is a flow chart of a method for obtaining sectional feature points of a multi-constraint blade section line according to the application;
FIG. 2 is a graph of the cross-sectional line discrete point curvature profile of a blade in accordance with the present application;
FIG. 3 is a schematic view of discrete points excluded by the curvature difference ratio constraint of the present application.
Detailed Description
Embodiments of the present application are described in further detail below with reference to the accompanying drawings.
Referring to fig. 1, the application discloses a multi-constraint blade section line partition characteristic point acquisition method, which comprises the following steps:
s1, generating a section line:
obtaining a blade section line from the blade solid model, wherein the blade section line is an intersection line between a reference plane and the blade solid model, and the reference plane is perpendicular to the extension direction of the blade body;
s2, generating a point set:
sequentially sampling the section lines of the blade along the extending direction of the section lines of the blade in a mode of equal arc length, taking the two-dimensional coordinates of each sampling point and the curvature value of the section line at the sampling point as a group of sampling arrays, and generating a point set for export;
s3, preprocessing a point set:
ordering the sampling arrays in the point set according to the sampling time sequence, uniformly amplifying the curvature values in the sampling arrays, and deriving a preprocessed point set;
s4, traversing all sampling arrays in the preprocessed point set, selecting one sampling point at any time, and taking the sampling point and two sampling points which are continuously arranged behind the sampling point as a group of data;
s5, angle constraint judgment:
the first point in the group points to the second point to form a first vector, the second point points to the third point to form a second vector, whether the included angle between the first vector and the second vector meets a preset angle threshold range or not is judged, if not, the current sampling point is discarded, and the step S4 is carried out, and if yes, the step S6 is carried out;
s6, constraint judgment of curvature difference ratio:
sequentially calculating absolute values of curvature difference values of two adjacent sampling points, calculating a ratio between the absolute values of the two curvature difference values, judging whether the ratio meets a preset ratio threshold range, if not, discarding the current sampling point, and turning to step S4, if so, turning to step S7;
s7, difference constraint judgment of curvature difference values:
sequentially calculating absolute values of curvature difference values of two adjacent sampling points, calculating a difference value between the two curvature difference absolute values, judging whether the difference value meets a preset difference value threshold range, if not, discarding the current sampling point, and turning to step S4, if so, turning to step S8;
s8, constraint judgment of the ratio of the curvature values:
calculating to obtain curvature value ratio of the third sampling point and the second sampling point and curvature value ratio of the first sampling point and the second sampling point, respectively judging whether the curvature value ratio of the first sampling point and the second sampling point is larger than a preset ratio threshold value, if so, outputting the current sampling point as a characteristic point, otherwise, discarding the current sampling point, and turning to step S4;
and S9, repeating the steps S4 to S8 until all the sampling points are processed, outputting all the characteristic points, and obtaining the boundary points of the front edge and the rear edge of the section line of the blade.
In this embodiment, the present application is directed to an analysis of a solid blade model, and a method for obtaining a blade cross-section line partition feature point of the present application will be described. The method for acquiring the sectional characteristic points of the blade section line mainly comprises the following five steps: cross section line acquisition, point set information derivation, data processing and constraint calculation.
Step (1) obtaining a section line
The specific process is as follows:
and generating a reference plane in UG, wherein the reference plane is perpendicular to the extension direction of the blade body, and the reference plane and the blade solid model are intersected to generate a blade section line.
Step (2) acquisition of Point set
The specific process is as follows:
a dense point set is generated on the obtained blade section line in a sampling mode with equal arc length, and the point-to-point distance is smaller than 0.02mm.
Step (3) Point set information derivation
The specific process is as follows:
the derived point set information content includes two-dimensional coordinates of points and curvature values of cross-section lines at the points, and each row of data fields is arranged in the order of 'X-coordinate', 'Y-coordinate', 'curvature values'.
Step (4) processing data
The specific process is as follows:
the points in the point set are sorted according to the nearest points, and the curvature values are amplified by the points in the point set, so that curvature abrupt changes can be observed in a graph with point serial numbers in the point set as an abscissa and curvature values as an ordinate, as shown in fig. 2. The magnification of the curvature should be such that the slope angle at the curvature abrupt change is 45 ° or more and 135 ° or less.
Step (5) calculating constraints
The specific process is as follows:
and traversing all the point sets, and calculating four constraints, namely an angle constraint, a curvature value ratio constraint, a curvature difference value ratio constraint and a curvature difference value difference constraint.
1) The angle constraint is that the first point in the group points to the second point to form a vector 1, the second point points to the third point to form a vector 2, and the included angle of the two vectors meets the angle constraint when the included angle is greater than or equal to 45 degrees and less than or equal to 135 degrees. The method has the function of filtering out rising edges and falling edges of curvature value mutation and obtaining a demarcation point for distinguishing the front edge from the rear edge.
2) The curvature difference ratio constraint is that the curvature difference ratio DR is calculated, and the mathematical expression of DR is shown in formula (1):
（1）
wherein ,express intra-group firstThe curvature value of one point is calculated,representing the curvature value of the second point in the group,the curvature value for the third point in the group is represented. DR (digital radiography)>10 or DR<0.1 satisfies the curvature difference ratio constraint. Its function is to filter the disturbances of the curvature jump points of the front and rear edges within the rectangular frame as shown in fig. 3.
3) The difference constraint of the curvature differences is the calculation of the difference of the curvature differences. The mathematical expression for calculating the difference D1 of curvature differences, D1, is shown in formula (2):
（2）
wherein ,a curvature value representing the first point in the group,representing the curvature value of the second point in the group,the curvature value for the third point in the group is represented.
The mathematical expression for calculating the difference D2 of curvature differences, D2, is shown in formula (3):
（3）
wherein ,a curvature value representing the first point in the group,representing the curvature value of the second point in the group,express intra-group firstCurvature values for three points. If D1 or D2 is greater than 0, the difference constraint of the curvature difference is satisfied. Its function is also to filter the disturbance of curvature jump points of the front and rear edges in the rectangular frame as shown in fig. 3, and find the characteristics of rising edge and falling edge of curvature value.
4) The ratio constraint of the curvature values, that is, the ratio of the curvature values of the third point to the second point should be greater than 1.5 or the ratio of the curvature values of the first point to the second point in the group should be greater than 1.5, satisfying the ratio constraint of the curvature values. Its function is also to filter the disturbances of curvature jump points of the front and rear edges in the rectangular frame as shown in fig. 3, and also to filter the abnormal points of curvature values which cannot be filtered by the above three constraints on the rising edge and the falling edge.
When all the point sets are traversed, four constraints are calculated, so that the front edge and the rear edge of the section line of the blade are obtained after the four constraints are met, and the front edge, the rear edge, the leaf basin and the leaf back area on the section line are divided.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The scheme in the embodiment of the application can be realized by adopting various computer languages, such as object-oriented programming language Java, an transliteration script language JavaScript and the like.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (8)
1. The multi-constraint blade section line partition characteristic point acquisition method is characterized by comprising the following steps of:
s1, generating a section line:
obtaining a blade section line from the blade solid model, wherein the blade section line is an intersection line between a reference plane and the blade solid model, and the reference plane is perpendicular to the extension direction of the blade body;
s2, generating a point set:
sequentially sampling the section lines of the blade along the extending direction of the section lines of the blade in a mode of equal arc length, taking the two-dimensional coordinates of each sampling point and the curvature value of the section line at the sampling point as a group of sampling arrays, and generating a point set for export;
s3, preprocessing a point set:
ordering the sampling arrays in the point set according to the sampling time sequence, uniformly amplifying the curvature values in the sampling arrays, and deriving a preprocessed point set;
s4, traversing all sampling arrays in the preprocessed point set, selecting one sampling point at any time, and taking the sampling point and two sampling points which are continuously arranged behind the sampling point as a group of data;
s5, angle constraint judgment:
the first point in the group points to the second point to form a first vector, the second point points to the third point to form a second vector, whether the included angle between the first vector and the second vector meets a preset angle threshold range or not is judged, if not, the current sampling point is discarded, and the step S4 is carried out, and if yes, the step S6 is carried out;
s6, constraint judgment of curvature difference ratio:
sequentially calculating absolute values of curvature difference values of two adjacent sampling points, calculating a ratio between the absolute values of the two curvature difference values, judging whether the ratio meets a preset ratio threshold range, if not, discarding the current sampling point, and turning to step S4, if so, turning to step S7;
s7, difference constraint judgment of curvature difference values:
sequentially calculating absolute values of curvature difference values of two adjacent sampling points, calculating a difference value between the two curvature difference absolute values, judging whether the difference value meets a preset difference value threshold range, if not, discarding the current sampling point, and turning to step S4, if so, turning to step S8;
s8, constraint judgment of the ratio of the curvature values:
calculating to obtain curvature value ratio of the third sampling point and the second sampling point and curvature value ratio of the first sampling point and the second sampling point, respectively judging whether the curvature value ratio of the first sampling point and the second sampling point is larger than a preset ratio threshold value, if so, outputting the current sampling point as a characteristic point, otherwise, discarding the current sampling point, and turning to step S4;
and S9, repeating the steps S4 to S8 until all the sampling points are processed, outputting all the characteristic points, and obtaining the boundary points of the front edge and the rear edge of the section line of the blade.
2. The method for obtaining the multi-constraint blade section line partition characteristic points according to claim 1, wherein in the step S2, the distance between adjacent sampling points is less than 0.02mm.
3. The method for obtaining the multi-constraint blade section line partition characteristic points according to claim 1, wherein in the step S3, curvature values in the sampling array are amplified uniformly, and the amplification factors are selected so that gradient angles of all curvature abrupt changes are greater than or equal to 45 degrees and less than or equal to 135 degrees.
4. The method for obtaining the multi-constraint blade section line segment feature points according to claim 1, wherein in step S5, the preset angle threshold ranges from 45 ° to 135 °.
5. The method for obtaining the multi-constraint blade section line partition feature points according to claim 1, wherein in step S6, the determination process of the curvature difference ratio constraint includes:
the curvature difference ratio is calculated by the following formula：
；
in the formula ,curvature value representing the i-th point, +.>Curvature value representing the i+1th point, +.>A curvature value representing the i+2th point;
if the ratio of curvature differencesGreater than 10, or the ratio of curvature differences +.>And if the curvature difference is smaller than 0.1, judging that the current sampling point meets the curvature difference ratio constraint.
6. The method for obtaining the multi-constraint blade section line partition feature points according to claim 1, wherein in step S7, the determining process of the difference constraint of the curvature difference values includes:
calculating the difference between the curvature differences：
；
wherein ,curvature value representing the i-th point, +.>Curvature value representing the i+1th point, +.>A curvature value representing the i+2th point;
calculating the difference between the curvature differences：
；
If D1 or D2 is larger than 0, judging that the current sampling point meets the difference constraint of the curvature difference value.
7. The method for obtaining the multi-constraint blade section line partition feature points according to claim 1, wherein in step S8, the preset ratio threshold is 1.5.
8. The method for obtaining the partitioned feature points of the cross-sectional line of the blade according to claim 1, wherein in the step S8, the front and rear edge partitioning points of the cross-sectional line of the blade are obtained according to all the feature points, and the front edge, the rear edge, the basin and the back area of the cross-sectional line of the blade are partitioned.
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